




'''simple docstring'''



import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

UpperCAmelCase_							:	Tuple	     =		(
    '''4S 3H 2C 7S 5H''',
    '''9D 8H 2C 6S 7H''',
    '''2D 6D 9D TH 7D''',
    '''TC 8C 2S JH 6C''',
    '''JH 8S TH AH QH''',
    '''TS KS 5S 9S AC''',
    '''KD 6S 9D TH AD''',
    '''KS 8D 4D 9S 4S''',  # pair
    '''8C 4S KH JS 4D''',  # pair
    '''QH 8H KD JH 8S''',  # pair
    '''KC 4H KS 2H 8D''',  # pair
    '''KD 4S KC 3H 8S''',  # pair
    '''AH 8S AS KC JH''',  # pair
    '''3H 4C 4H 3S 2H''',  # 2 pairs
    '''5S 5D 2C KH KH''',  # 2 pairs
    '''3C KH 5D 5S KH''',  # 2 pairs
    '''AS 3C KH AD KH''',  # 2 pairs
    '''7C 7S 3S 7H 5S''',  # 3 of a kind
    '''7C 7S KH 2H 7H''',  # 3 of a kind
    '''AC KH QH AH AS''',  # 3 of a kind
    '''2H 4D 3C AS 5S''',  # straight (low ace)
    '''3C 5C 4C 2C 6H''',  # straight
    '''6S 8S 7S 5H 9H''',  # straight
    '''JS QS 9H TS KH''',  # straight
    '''QC KH TS JS AH''',  # straight (high ace)
    '''8C 9C 5C 3C TC''',  # flush
    '''3S 8S 9S 5S KS''',  # flush
    '''4C 5C 9C 8C KC''',  # flush
    '''JH 8H AH KH QH''',  # flush
    '''3D 2H 3H 2C 2D''',  # full house
    '''2H 2C 3S 3H 3D''',  # full house
    '''KH KC 3S 3H 3D''',  # full house
    '''JC 6H JS JD JH''',  # 4 of a kind
    '''JC 7H JS JD JH''',  # 4 of a kind
    '''JC KH JS JD JH''',  # 4 of a kind
    '''2S AS 4S 5S 3S''',  # straight flush (low ace)
    '''2D 6D 3D 4D 5D''',  # straight flush
    '''5C 6C 3C 7C 4C''',  # straight flush
    '''JH 9H TH KH QH''',  # straight flush
    '''JH AH TH KH QH''',  # royal flush (high ace straight flush)
)

UpperCAmelCase_							:	Optional[int]	     =		(
    ('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
    ('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
    ('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
    ('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
    ('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
    ('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
    ('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
    ('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
    ('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
    ('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
    ('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
    ('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
    ('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
    ('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
    ('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
    ('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
    ('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
    ('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
    ('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
    ('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
    ('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
    ('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
    ('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
    ('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
    ('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
    ('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
    ('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)

UpperCAmelCase_							:	List[Any]	     =		(
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', True),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', False),
    ('''AS 3S 4S 8S 2S''', True),
)

UpperCAmelCase_							:	Any	     =		(
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', False),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', True),
)

UpperCAmelCase_							:	Any	     =		(
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
    ('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

UpperCAmelCase_							:	Any	     =		(
    ('''JH AH TH KH QH''', 0),
    ('''JH 9H TH KH QH''', 0),
    ('''JC KH JS JD JH''', 7),
    ('''KH KC 3S 3H 3D''', 6),
    ('''8C 9C 5C 3C TC''', 0),
    ('''JS QS 9H TS KH''', 0),
    ('''7C 7S KH 2H 7H''', 3),
    ('''3C KH 5D 5S KH''', 2),
    ('''QH 8H KD JH 8S''', 1),
    ('''2D 6D 9D TH 7D''', 0),
)

UpperCAmelCase_							:	Optional[Any]	     =		(
    ('''JH AH TH KH QH''', 2_3),
    ('''JH 9H TH KH QH''', 2_2),
    ('''JC KH JS JD JH''', 2_1),
    ('''KH KC 3S 3H 3D''', 2_0),
    ('''8C 9C 5C 3C TC''', 1_9),
    ('''JS QS 9H TS KH''', 1_8),
    ('''7C 7S KH 2H 7H''', 1_7),
    ('''3C KH 5D 5S KH''', 1_6),
    ('''QH 8H KD JH 8S''', 1_5),
    ('''2D 6D 9D TH 7D''', 1_4),
)







def       _UpperCamelCase			()-> Dict:




     '''simple docstring'''





     __snake_case       ,     __snake_case            =	randrange(len(_lowerCamelCase						)						), randrange(len(_lowerCamelCase						)						)
     __snake_case            =	['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
     __snake_case       ,     __snake_case            =	SORTED_HANDS[play], SORTED_HANDS[oppo]
     return hand, other, expected







def       _UpperCamelCase			(_lowerCamelCase				:  int = 1_00						)-> Any:




     '''simple docstring'''





     return (generate_random_hand() for _ in range(_lowerCamelCase						))







@pytest.mark.parametrize('''hand, expected''' ,						_lowerCamelCase						)
def       _UpperCamelCase			(_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  Optional[Any]						)-> str:




     '''simple docstring'''





     assert PokerHand(_lowerCamelCase						)._is_flush() == expected







@pytest.mark.parametrize('''hand, expected''' ,						_lowerCamelCase						)
def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  int						)-> Any:




     '''simple docstring'''





     assert PokerHand(_lowerCamelCase						)._is_straight() == expected







@pytest.mark.parametrize('''hand, expected, card_values''' ,						_lowerCamelCase						)
def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	PokerHand(_lowerCamelCase						)
     assert player._is_five_high_straight() == expected
     assert player._card_values == card_values







@pytest.mark.parametrize('''hand, expected''' ,						_lowerCamelCase						)
def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  Dict						)-> Union[str, Any]:




     '''simple docstring'''





     assert PokerHand(_lowerCamelCase						)._is_same_kind() == expected







@pytest.mark.parametrize('''hand, expected''' ,						_lowerCamelCase						)
def       _UpperCamelCase			(_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[Any]						)-> int:




     '''simple docstring'''





     assert PokerHand(_lowerCamelCase						)._hand_type == expected







@pytest.mark.parametrize('''hand, other, expected''' ,						_lowerCamelCase						)
def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Optional[int]						)-> List[Any]:




     '''simple docstring'''





     assert PokerHand(_lowerCamelCase						).compare_with(PokerHand(_lowerCamelCase						)						) == expected







@pytest.mark.parametrize('''hand, other, expected''' ,						generate_random_hands()						)
def       _UpperCamelCase			(_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  List[Any]						)-> Tuple:




     '''simple docstring'''





     assert PokerHand(_lowerCamelCase						).compare_with(PokerHand(_lowerCamelCase						)						) == expected







def       _UpperCamelCase			()-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	[PokerHand(_lowerCamelCase						) for hand in SORTED_HANDS]
     __snake_case            =	poker_hands.copy()
     shuffle(_lowerCamelCase						)
     __snake_case            =	chain(sorted(_lowerCamelCase						)						)
     for index, hand in enumerate(_lowerCamelCase						):
          assert hand == poker_hands[index]







def       _UpperCamelCase			()-> List[Any]:




     '''simple docstring'''





     __snake_case            =	[PokerHand('''2D AC 3H 4H 5S'''						), PokerHand('''2S 3H 4H 5S 6C'''						)]
     pokerhands.sort(reverse=_lowerCamelCase						)
     assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"







def       _UpperCamelCase			()-> List[str]:




     '''simple docstring'''





     __snake_case            =	PokerHand('''2C 4S AS 3D 5C'''						)
     __snake_case            =	True
     __snake_case            =	[5, 4, 3, 2, 14]
     for _ in range(10						):
          assert pokerhand._is_five_high_straight() == expected
          assert pokerhand._card_values == expected_card_values







def       _UpperCamelCase			()-> Dict:




     '''simple docstring'''





     __snake_case            =	0
     __snake_case            =	os.path.abspath(os.path.dirname(_lowerCamelCase						)						)
     __snake_case            =	os.path.join(_lowerCamelCase ,						'''poker_hands.txt'''						)
     with open(_lowerCamelCase						) as file_hand:
          for line in file_hand:
               __snake_case            =	line[:14].strip()
               __snake_case            =	line[15:].strip()
               __snake_case       ,     __snake_case            =	PokerHand(_lowerCamelCase						), PokerHand(_lowerCamelCase						)
               __snake_case            =	player.compare_with(_lowerCamelCase						)
               if output == "Win":
                    answer += 1
     assert answer == 3_76








"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





